2019 Fiscal Year Research-status Report
Machine Learning on Large Graphs
| Project/Area Number |
18K11434
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| Research Institution | Kyoto University |
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Principal Investigator |
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| Project Period (FY) |
2018-04-01 – 2021-03-31
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| Keywords | large graph / graph Laplacian / hypergraph / sparsistency |
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| Outline of Annual Research Achievements |
The target of the research is to derive statistically sound models to learn from a large graphs, and its related extensions and applications. In this year, we have discovered a statistically sound model to learn from an extension of hypergraph. That is the set of nodes with more than two-way relationships among them. Previous works are not sound when the sizes of hyperedges go to infinity. This is, realistic in large hypergraphs under reasonable assumption.
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| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
We have found promising result surrounding the main topics of learning on large graphs, with applications. We still continue to look for central results on large graphs.
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| Strategy for Future Research Activity |
In this year, we plan to continue to work on the target of learning on large graphs with more general semantics of graphs, and their applications in Bioinformatics such as biological networks, molecular graphs and so on.
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| Causes of Carryover |
We could not spend the budget on business trips this year as planned due to the lack of activities in our side. We will continue more research activities this year that will use the budget.
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